Diamond Price Forecasting: Unraveling the Influence of Features and Trends

Diamond Price Forecasting: Unraveling the Influence of Features and Trends

Mrs. Sneha Kanwade, Ms. Arti Singh, Ms. Hetvi Patel, Ms. Pranjal Chaudhari, Ms. Akshada Patil

Abstract:

Predicting diamond prices with accuracy is crucial for the jewelry business since it helps both sellers and buyers. A strong machine learning model that can forecast diamond prices with an impressive degree of accuracy is the goal of the Diamond Feature and Price Prediction. The study uses advanced regression techniques to model the complex relationship between several diamond qualities, such as carat weight, cut quality, color, and clarity, and their respective market values.

The enormous dataset used in the study captures these attributes. A methodical approach is utilized to contrast different machine learning methods, including Ridge regression, Lasso regression, ElasticNet, and Linear regression. Preprocessing is applied to the dataset in order to handle missing values, normalize features, and efficiently encode categorical variables. The significance of each characteristic is determined by evaluating its importance.

By producing the lowest prediction errors and the highest R-squared value, the gradient boosting technique outperforms other models, as demonstrated by experimental findings. This model is an example of how it can provide accurate diamond pricing predictions, which will help jewelers and customers make well-informed judgements. The results highlight the most important variables that affect diamond valuation and emphasize how important characteristics like carat weight and cut quality are in setting the final price.

Keywords: Diamond prices, Machine learning, Regression techniques, Carat weight, Cut quality, Color, Clarity, Lasso Regression, Ridge Regression, ElasticNet, Linear regression, Preprocessing, Normalization, Categorical variables, R-squared value